{"id":"W4389002969","doi":"10.1093/gbe/evad211","title":"Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution","year":2023,"lang":"en","type":"article","venue":"Genome Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Phylogenetic tree; Biology; Phylogenetic comparative methods; Divergence (linguistics); Trait; Expression (computer science); Evolutionary biology; Gene; Set (abstract data type); Phylogenetics; Computational biology; Gene expression; Genetics; Statistics; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05795442,0.002499511,0.002394502,0.005680162,0.001626932,0.003670432,0.003296783,0.00354165,0.001169886],"category_scores_gemma":[0.1051552,0.0009195804,0.003715284,0.003886597,0.002344699,0.003824496,0.00243177,0.003200878,0.0004620144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003336171,"about_ca_system_score_gemma":0.001955847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005454598,"about_ca_topic_score_gemma":0.006358468,"domain_scores_codex":[0.977427,0.01635201,0.001341974,0.002843303,0.001516244,0.0005194945],"domain_scores_gemma":[0.8753231,0.1108745,0.004424265,0.005923144,0.002509881,0.0009451067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007428549,0.0001961507,0.05838225,0.000511949,0.00254625,0.0001831815,0.0004310088,0.9005445,0.002574716,0.007252341,0.0008130764,0.02582179],"study_design_scores_gemma":[0.00006121615,0.0004399302,0.009190075,0.0001077174,0.0002020421,0.0001374853,0.0001866294,0.9751986,0.001480197,0.01223519,0.0006731289,0.0000878777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7238706,0.004239439,0.2655194,0.001175709,0.0001309598,0.0002070316,0.001714947,0.001113379,0.002028619],"genre_scores_gemma":[0.8928469,0.001058978,0.1016241,0.000317038,0.00006217342,0.0003134493,0.003152394,0.0003273584,0.0002975988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05795442,"threshold_uncertainty_score":0.3064959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542684295846601,"score_gpt":0.3010901160379307,"score_spread":0.2556632730794647,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}